4 papers
DarwinTOD: LLM-driven Lifelong Self-evolution for Task-oriented Dialog Systems
Shuyu Zhang, Yujie Liu, Xinru Wang +3
Traditional task-oriented dialog systems are unable to evolve from ongoing interactions or adapt to new domains after deployment, that is a critical limitation in real-world dynami…
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST
Shuyu Zhang, Yifan Wei, Xinru Wang +5
Zero-shot Dialog State Tracking (zs-DST) is essential for enabling Task-Oriented Dialog Systems (TODs) to generalize to new domains without costly data annotation. A central challe…
DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual-Systems
Shuyu Zhang, Yifan Wei, Jialuo Yuan +4
Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts, leading to inefficient exploration and suboptimal performance…
Generalizable Self-Evolving Memory for Automatic Prompt Optimization
Guanbao Liang, Yuanchen Bei, Sheng Zhou +5
Automatic prompt optimization is a promising approach for adapting large language models (LLMs) to downstream tasks, yet existing methods typically search for a specific prompt spe…